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Improving Robustness of Image Tampering Detection for Compression

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11295))

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Abstract

The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

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Author information

Authors and Affiliations

  1. XLIM Research Institute (UMR CNRS 7252), University of Poitiers, Poitiers, France

    Boubacar Diallo, Thierry Urruty, Pascal Bourdon & Christine Fernandez-Maloigne

Authors
  1. Boubacar Diallo
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  2. Thierry Urruty
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  3. Pascal Bourdon
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  4. Christine Fernandez-Maloigne
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Corresponding author

Correspondence to Boubacar Diallo .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

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Diallo, B., Urruty, T., Bourdon, P., Fernandez-Maloigne, C. (2019). Improving Robustness of Image Tampering Detection for Compression. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-05710-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05709-1

  • Online ISBN: 978-3-030-05710-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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